Schema Induction

Schema induction focuses on automatically learning structured representations of events and their relationships, aiming to capture knowledge about how events unfold in various scenarios. Current research emphasizes leveraging large language models (LLMs) to generate and refine these schemas, often incorporating techniques like incremental prompting, graph convolutional networks, and human-in-the-loop approaches to improve accuracy and completeness. This work has significant implications for diverse applications, including predictive analytics (e.g., supply chain risk assessment), knowledge discovery from text, and enhancing the interpretability of in-context learning in LLMs.